The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also be inventions.
Accurate retrieval of semantically similar objects has become increasingly more complex due to the sheer number of semantically described objects necessary for comparison with a particular object in order to find semantically similar objects and the numerous approaches to defining the objects. Additionally, there are a variety of possible formats for the descriptions of objects and organizational models to reference from providers of the objects which adds to the complexity. The complexity increases as the number of objects and the number of different organizational models for the objects grows.
For example, it has become difficult for a reseller to retrieve semantically similar objects when similar objects are described differently by each provider, and each provider may have completely different organizational model for grouping of the objects. Continuing with the example, a reseller may need to return results for a search query with an object described as “plasma tv” and an object of “tv” with the attribute of “plasma” and the information for the objects could be modeled in two entirely different organizational structures.
The retrieval of accurate information and subsequent delivery of semantically similar objects to the user system has been and continues to be a goal of search and/or knowledge management systems in a computing environment. The ability to scale well in light of the number of objects and comparisons necessary has been and continues to be a goal for approaches to retrieval of semantically similar objects. Accordingly, it is desirable to provide techniques to improve the accuracy of semantic matching methods that scales well in a computing environment.